Image super-resolution (SR) is the task of recovering details of a high-resolution (HR) image from its low-resolution (LR) counterpart. Most SR methods [1]}, [2]}, [3]} assume an ideal bicubic downsampling kernel, which is different from real-world degradations. This degradation mismatch makes those approaches unpractical in real-world scenarios. Recently, some attempts [4]}, [5]}, [6]}, [7]} are proposed to address real-world super-resolution (RWSR). Nevertheless, existing RWSR methods can only perform restoration with fixed one-to-one mapping. In other words, they lack the flexibility, i.e., interactive modulation [8]}, [9]}, to alter the outputs by adjusting different restoration strength levels.
